def on_bar_minx(self, bar):
self.am.updateBar(bar)
if not self.am.inited:
return
# 涨跌停时暂停交易
if bar.close == bar.UpperLimitPrice:
self.pause = True
to_log(self.vtSymbol + ' 已涨停，该品种dali策略暂停交易')
if bar.close == bar.LowerLimitPrice:
self.pause = True
to_log(self.vtSymbol + ' 已跌停，该品种dali策略暂停交易')
if self.paused == True and self.backtest == False:
self.counter += 1
if self.counter == 3:
filename = get_dss() + 'gateway_closed.csv'
if os.path.exists(filename):
pass
else:
send_email(get_dss(), self.vtSymbol + ' 挂单未成交！！！', '')
return
self.calculateIndicator() # 计算指标
self.generateSignal(bar) # 触发信号，产生交易指令
def __init__(self):
"""Constructor"""
self.dss = get_dss()
self.portfolio = None # 一对一
self.portfolioValue = 100E4
self.startDt = None
self.endDt = None
self.currentDt = None
self.barDict = OrderedDict()
self.min1barDict = OrderedDict()
self.min5barDict = OrderedDict()
self.min15barDict = OrderedDict()
self.min30barDict = OrderedDict()
self.result = None
self.resultList = []
self.tradeDict = OrderedDict()
# 加载配置
config = open(get_dss() + 'fut/cfg/config.json')
setting = json.load(config)
symbols = setting['symbols']
self.vtSymbol_list = symbols.split(',')
def smile_symbol():
"""期权微笑曲线"""
now = datetime.now()
# today = now.strftime('%Y-%m-%d %H:%M:%S')
today = now.strftime('%Y-%m-%d')
# today = '2020-06-22'
fn = get_dss() + 'opt/' + today[:7] + '_sigma.csv'
df = pd.read_csv(fn)
df = df[df.date == today]
df = df.drop_duplicates(subset=['term'], keep='last')
# print(df.head())
fn = get_dss() + 'fut/cfg/opt_mature.csv'
df_IO = pd.read_csv(fn)
df_IO = df_IO[df_IO.flag == df_IO.flag] # 筛选出不为空的记录
df = df[df.term.isin(list(df_IO.symbol))]
print(df.term)
for i, row in df.iterrows():
c_curve_dict = eval(row.c_curve)
p_curve_dict = eval(row.p_curve)
df1 = pd.DataFrame([c_curve_dict, p_curve_dict])
df1 = df1.T
df1.columns = ['call', 'put']
df1.plot()
plt.title(today + '_' + row.term)
fn = 'static/smile_' + row.term + '.jpg'
plt.savefig(fn)
def fresh_focus():
all = 0
now = datetime.now()
today = now.strftime('%Y-%m-%d')
dirname = get_dss() + 'fut/engine/focus/'
listfile = os.listdir(dirname)
for fn in listfile:
if fn.startswith('focus'):
fn = dirname + fn
df = pd.read_csv(fn)
if len(df) == 0:
continue
rec = df.iloc[-1,:]
book_list = eval(rec.book_list)
pnl = 0
for book in book_list:
fn1 = get_dss() + 'fut/engine/book/book_' + book + '.csv'
if os.path.exists(fn1) == False:
continue
df1 = pd.read_csv(fn1)
row = df1.iloc[-1,:]
pnl += row.pnl
rec.datetime = today + ' 15:00:00'
rec.pnl = pnl
df = pd.DataFrame([rec])
df.to_csv(fn, index=False, header=None, mode='a')
all += pnl
df = pd.DataFrame([[today, all]], columns=['date','pnl'])
fn = get_dss() + 'fut/engine/focus/all.csv'
df.to_csv(fn, index=False, header=None, mode='a')
def warehouse_product(kind, p_list):
dirname = get_dss() + 'info/warehouse/img/'
fn = os.path.join(get_dss(), 'info/warehouse/warehouse_' + kind + '.csv')
df_a = pd.read_csv(fn)
date_list = sorted(set(df_a.date))
date_list = date_list[-60:]
# print(date_list)
for p in p_list:
df = df_a[(df_a.date.isin(date_list)) & (df_a.symbol == p)]
df = df.set_index('date')
df = df.sort_index()
# print(df.head())
df0 = df.copy()
df0['value'] = df0['receipt']
df0.index.name = 'receipt'
if kind == 'czce':
df1 = df.copy()
df1['value'] = df1['forecast']
df1.index.name = 'forecast'
show_21(dirname, [df0], [df1], p, False, p)
else:
show_11(dirname, [df0], p, False, p)
# 生成pdf
listfile = os.listdir(dirname)
img_list = []
for fn in listfile:
img_list.append(fn)
img2pdf(dirname, 'warehouse_' + kind + '.pdf', img_list)
def pcp():
r = 0.03
T = 15/365
S = 3912
result = []
dt = '2020-04-30'
x_list = range(3150,4700,50)
# print(*s_list)
fn = get_dss() + 'backtest/IO/' + 'IO' + year + '.csv'
df = pd.read_csv(fn)
# print(df.head())
for x in x_list:
symbol_c = 'IO2005-C-' + str(x)
symbol_p = 'IO2005-P-' + str(x)
# print(symbol_c)
df_c = df[(df.date == dt) & (df.symbol == symbol_c)]
df_p = df[(df.date == dt) & (df.symbol == symbol_p)]
row_c = df_c.iloc[0,:]
row_p = df_p.iloc[0,:]
pSc = int( row_p.close + S -row_c.close )
pSc_final = int( pSc * (1 + r*T) )
result.append( [S, row_c.close, row_p.close, x, pSc, pSc_final, x-pSc_final] )
df = pd.DataFrame(result, columns=['S', 'call', 'put', 'X', 'pSc', 'pSc_final', 'diff'])
fn = get_dss() + 'backtest/IO/p1.csv'
df.to_csv(fn, index=False)
def extract_trade():
"""从成交反馈文件中提取最新成交记录，追加到got_trade文件中"""
# 提取当前日期下的成交记录
now = datetime.now()
today = int(now.strftime('%Y%m%d'))
# today = 20201217
fn = get_dss() + 'fut/engine/gateway_trade.csv'
df = pd.read_csv(fn)
df = df[df.TradingDay >= today] # 当日交易记录
df = df.drop_duplicates()
# print(df)
n1 = len(df)
if n1 == 0:
return
# 对数据进行预处理，符合文件字段的要求
date_list = list(df.TradingDay.astype('str'))
date_list = [x[:4] + '-' + x[4:6] + '-' + x[6:8] for x in date_list]
df['TradingDay'] = date_list
del df['SysID']
df['wipe'] = 'no'
df['before'] = ''
df['current'] = 'secure'
df['got'] = 'no'
df['seq'] = 0
# 在原文件的基础上合并新的成交记录，去重后重新写入文件
fn0 = get_dss() + 'fut/engine/book/got_trade.csv'
df0 = pd.read_csv(fn0)
df0 = pd.concat([df0, df], sort=False)
df0 = df0.drop_duplicates(subset=['OrderID', 'TradeID', 'TradingDay'],
keep='first')
df0['seq'] = range(1, len(df0) + 1)
df0.to_csv(fn0, index=False)
def calc_d_base(date):
# 计算并保存d_base
r = []
symbol_list = get_symbols_quote()
for symbol in symbol_list:
if symbol[:2] != 'IO':
continue
try:
basic = symbol[:6]
strike = get_contract(symbol).strike
symbol_c = basic + '-C-' + str(strike)
symbol_p = basic + '-P-' + str(strike)
fn = get_dss() + 'fut/bar/day_' + symbol_c + '.csv'
df_c = pd.read_csv(fn)
fn = get_dss() + 'fut/bar/day_' + symbol_p + '.csv'
df_p = pd.read_csv(fn)
d_base = df_c.iat[-1, 5] + df_p.iat[-1, 5]
r.append([date, basic, strike, d_base])
except Exception as e:
# s = traceback.format_exc()
# to_log(s)
pass
df = pd.DataFrame(r, columns=['date', 'basic', 'strike', 'd_base'])
fn = get_dss() + 'opt/sdiffer_d_base.csv'
if os.path.exists(fn):
df.to_csv(fn, index=False, mode='a', header=False)
else:
df.to_csv(fn, index=False)
def smile():
"""期权微笑曲线"""
fn = get_dss() + 'fut/cfg/opt_mature.csv'
df_opt = pd.read_csv(fn)
# for pz in set(df_opt.pz):
for pz in ['IO']:
print(pz)
df = df_opt[(df_opt.pz == pz) &
(df_opt.flag == df_opt.flag)] # 筛选出不为空的记录
df = df.sort_values('symbol')
symbol_list = list(df.symbol)
cur_if = 'IF' + symbol_list[0][2:]
# print(cur_if)
fn = get_dss() + 'fut/bar/day_' + cur_if + '.csv'
df = pd.read_csv(fn)
row = df.iloc[-1, :]
strike = row.open * 0.5 + row.close * 0.5
strike = int(round(strike / 1E4, 2) * 1E4)
atm = strike # 获得平值
# print(atm)
smile_pz(pz, symbol_list, atm, 'call')
smile_pz(pz, symbol_list, atm, 'put')
smile_pz(pz, symbol_list, atm, 'avg')
def price_signal(dss, day):
df = get_daily(dss, day)
# print(df.head(3))
# print(len(df))
df1 = df[df.p_change > 6.18]
# print(len(df1))
# print(df1.head(3))
r = []
for i, row in df1.iterrows():
# print(row.code, row['name'], row.p_change)
if be_bottom(row.code, day):
df2 = pro.concept_detail(ts_code=get_ts_code(row.code))
concept = str(df2.concept_name.tolist())
r.append([day, row.code, row['name'], row.p_change, concept])
# 拷贝数据文件到下载目录，以备下载。windows下一定要用\\
ins = 'copy ' + get_dss(
) + 'hfq\\' + row.code + '.csv ' + 'C:\\Users\\Administrator\\Downloads\\' + row.code + '_' + row[
'name'] + '.csv '
print(ins)
os.system(ins)
#break
df = pd.DataFrame(r, columns=['date', 'code', 'name', 'change', 'concept'])
fn = get_dss() + 'csv/price_signal.csv'
df.to_csv(fn, index=False, mode='a')
return r
def pandian_dali(today):
# 读取value_dali文件
fn_value = get_dss() + 'fut/engine/star/value_dali.csv'
if os.path.exists(fn_value):
df_value = pd.read_csv(fn_value)
else:
return
fn_param = get_dss() + 'fut/engine/dali/signal_dali_param.csv'
df_param = pd.read_csv(fn_param)
# df_param = df_param.set_index('pz')
pz_list = list(df_param.pz)
print(pz_list)
r = []
for pz in pz_list:
# 更新该品种的最新数据
fn_signal = get_dss() + 'fut/engine/dali/signal_dali_' +'multi'+ '_var_' + pz + '.csv'
if os.path.exists(fn_signal):
df = pd.read_csv(fn_signal)
rec = df.iloc[-1,:]
# print(rec)
cur_value = int(rec.pnl_net)
net_pos = rec.unit
multi_duo_list = eval(rec.price_duo_list)
multi_kong_list = eval(rec.price_kong_list)
r.append( [today,pz,cur_value,net_pos,multi_duo_list,multi_kong_list] )
df = pd.DataFrame(r)
df.to_csv(fn_value, index=False, mode='a', header=None)
def fut():
symbol = 'ag2006'
filename = get_dss() + 'fut/put/min1_' + symbol + '.csv'
df = pd.read_csv(filename, dtype='str')
r_q = [list(df.columns)]
for i, row in df.iterrows():
r_q.append(list(row))
filename = get_dss() + 'fut/put/rec/min5_' + symbol + '.csv'
df = pd.read_csv(filename, dtype='str')
r_t = [list(df.columns)]
row = df.iloc[-1, :]
r_t.append(list(row))
filename = get_dss() + 'fut/engine/engine_deal.csv'
df = pd.read_csv(filename, dtype='str')
r = []
for i, row in df.iterrows():
r.append(list(row))
r.append(list(df.columns))
r = list(reversed(r))
r = r[:12]
return render_template("fut.html",
title=symbol,
rows_q=r_q,
rows_t=r_t,
rows=r)
def calc_die():
now = datetime.now()
today = now.strftime('%Y-%m-%d')
today = '2020-06-12'
fn = get_dss() + 'opt/' + today[:7] + '.csv'
df = pd.read_csv(fn)
df = df[df.Localtime > today+' 11:00:00']
df = df[df.Localtime < today+' 12:00:00']
df = df.set_index('Instrument')
r = []
if len(df) > 0:
r += die_IO(df, today)
r += die_m(df, today)
r += die_RM(df, today)
r += die_MA(df, today)
r += die_CF(df, today)
df2 = pd.DataFrame(r, columns=['date', 'term', 'c/p', 's1', 'c1', 's2', 'c2', 's3', 'c3', 'cost'])
df2 = df2[(df2.cost < 0) & (df2.cost != float('-inf'))]
fn = get_dss() + 'opt/die.csv'
df2.to_csv(fn, index=False)
return True
else:
return False
def calc_pcp():
now = datetime.now()
today = now.strftime('%Y-%m-%d')
# today = '2020-06-12'
# fn = get_dss() + 'opt/' + today[:7] + '_greeks.csv'
fn = get_dss() + 'opt/' + today[:7] + '.csv'
df = pd.read_csv(fn)
df = df[df.Localtime > today+' 11:00:00']
df = df[df.Localtime < today+' 12:00:00']
df = df.set_index('Instrument')
r = []
if len(df) > 0:
r += pcp_IO(df, today)
r += pcp_m(df, today)
r += pcp_RM(df, today)
r += pcp_MA(df, today)
r += pcp_CF(df, today)
fn = get_dss() + 'opt/pcp.csv'
df2 = pd.DataFrame(r, columns=['date', 'type', 'term', 'X', 'S', 'call', 'put', 'pSc', 'diff', 'rt'])
df2 = df2.sort_values('type')
df2.to_csv(fn, index=False)
return True
else:
return False
def get_trade():
"""从成交反馈文件中提取期权成交记录，追加到opt_trade文件中"""
now = datetime.now()
today = int(now.strftime('%Y%m%d'))
fn = get_dss() + 'fut/engine/gateway_trade.csv'
df = pd.read_csv(fn)
df = df[df.TradingDay == today] # 当日交易记录
df = df[df.InstrumentID.str.len() >= 9] # 期权合约
# print(df)
if len(df) > 0:
df = df.drop_duplicates()
del df['OrderID']
del df['SysID']
df['book'] = ''
df['portfolio'] = ''
df['margin'] = 0
date_list = list(df.TradingDay.astype('str'))
date_list = [x[:4] + '-' + x[4:6] + '-' + x[6:8] for x in date_list]
df['TradingDay'] = date_list
# print(df)
fn = get_dss() + 'fut/engine/opt/opt_trade.csv'
if os.path.exists(fn):
df.to_csv(fn, index=False, header=None, mode='a')
else:
df.to_csv(fn, index=False)
def compass(date):
listfile = os.listdir(dirname)
for fn in listfile:
os.remove(dirname + fn)
d2 = get_dss() + 'info/hold/img/'
listfile = os.listdir(d2)
for fn in listfile:
os.remove(d2 + fn)
fn = get_dss() + 'fut/cfg/opt_mature.csv'
df2 = pd.read_csv(fn)
df2 = df2[pd.notnull(df2.flag)]
fn_list = []
for func in [CF, m, RM, al, IO]:
# for func in [m]:
try:
fn_list += func(date, df2)
except Exception as e:
s = traceback.format_exc()
to_log(s)
# print(fn_list)
send_email(get_dss(), 'compass', '', fn_list)
def proc_rec_mature(rec, fname):
dss = get_dss()
pos_dict = eval(rec.posDict)
close_dict = eval(rec.closeDict)
now = datetime.now()
today = now.strftime('%Y-%m-%d')
rec.date = today
s_list = []
# 对于已到期的期权合约(IO除外)，将持仓置为0，并清理；若有行权，添加行权记录到got_trade文件
for symbol in pos_dict:
pos = pos_dict[symbol]
c = get_contract(symbol)
# 目前期货合约暂不处理（返回值为None）；回头也返回一个到期日，过期作清零处理
if c.mature is not None:
# 到期的，都要进行清零处理
if c.mature <= today:
s_list.append(symbol)
if c.be_opt and symbol[:2] != 'IO' and pos != 0:
size = c.size
if c.exchangeID == 'CZCE': # tick数据的特点，需对郑商所数据进行特殊处理
size = 1
settle = get_tick(c.basic)['AveragePrice'] / size
fn = get_dss() + 'fut/engine/book/got_trade.csv'
df = pd.read_csv(fn)
exercise = [
'', c.exchangeID, c.basic, 'Open',
int(time.time()),
round(settle, 4),
int(time.time()), '15:00:01', today,
abs(pos), 'no', '',
'hedge' if 'hedge' in fname else 'secure', 'no',
len(df) + 1
]
if (pos > 0 and c.opt_flag == 'C' and settle >= c.strike ) or \
(pos < 0 and c.opt_flag == 'P' and settle <= c.strike ):
# 以strike作为价格买开仓basic
exercise[0] = 'Buy'
if (pos > 0 and c.opt_flag == 'P' and settle <= c.strike ) or \
(pos < 0 and c.opt_flag == 'C' and settle >= c.strike ):
# 以strike作为价格卖开仓basic
exercise[0] = 'Sell'
if exercise[0] != '':
df = pd.DataFrame([exercise])
df.to_csv(fn, index=False, header=None, mode='a')
if s_list != []:
# 清零处理
for s in s_list:
if s in pos_dict:
pos_dict.pop(s)
if s in close_dict:
close_dict.pop(s)
rec.posDict = str(pos_dict)
rec.closeDict = str(close_dict)
df = pd.DataFrame([rec])
df.to_csv(fname, index=False, header=None, mode='a')
def save_sdiffer(date, date_pre, basic_m0, basic_m1, atm, stat):
fn = get_dss() + 'fut/bar/min5_' + basic_m0 + '-C-' + str(atm) + '.csv'
df_m0_c = pd.read_csv(fn)
df_m0_c_pre = df_m0_c[df_m0_c.date == date_pre]
df_m0_c = df_m0_c[df_m0_c.date == date]
fn = get_dss() + 'fut/bar/min5_' + basic_m0 + '-P-' + str(atm) + '.csv'
df_m0_p = pd.read_csv(fn)
df_m0_p_pre = df_m0_p[df_m0_p.date == date_pre]
df_m0_p = df_m0_p[df_m0_p.date == date]
fn = get_dss() + 'fut/bar/min5_' + basic_m1 + '-C-' + str(atm) + '.csv'
if os.path.exists(fn) == False:
return
df_m1_c = pd.read_csv(fn)
df_m1_c_pre = df_m1_c[df_m1_c.date == date_pre]
df_m1_c = df_m1_c[df_m1_c.date == date]
if len(df_m1_c_pre) == 0:
return
fn = get_dss() + 'fut/bar/min5_' + basic_m1 + '-P-' + str(atm) + '.csv'
df_m1_p = pd.read_csv(fn)
df_m1_p_pre = df_m1_p[df_m1_p.date == date_pre]
df_m1_p = df_m1_p[df_m1_p.date == date]
# print(basic_m0, basic_m1)
# print(atm)
# print(df_m1_c_pre.head())
# print(df_m1_p_pre.head())
d_base_m1 = df_m1_c_pre.iat[-1, 5] + df_m1_p_pre.iat[-1, 5]
d_base_m0 = df_m0_c_pre.iat[-1, 5] + df_m0_p_pre.iat[-1, 5]
df_m1_c = df_m1_c.reset_index()
df_m1_p = df_m1_p.reset_index()
df_m0_c = df_m0_c.reset_index()
df_m0_p = df_m0_p.reset_index()
df_m1_c['diff_m1'] = df_m1_c.close + df_m1_p.close - d_base_m1
df_m0_c['diff_m0'] = df_m0_c.close + df_m0_p.close - d_base_m0
df_m1_c['differ'] = df_m1_c.diff_m1 - df_m0_c.diff_m0
df_m1_c['pz'] = 'IO'
df_m1_c['basic_m0'] = basic_m0
df_m1_c['basic_m1'] = basic_m1
df_m1_c['atm'] = atm
df_m1_c['diff_m0'] = df_m0_c['diff_m0']
df_m1_c['stat'] = stat
df2 = df_m1_c[[
'date', 'time', 'pz', 'basic_m0', 'basic_m1', 'atm', 'diff_m0',
'diff_m1', 'differ', 'stat'
]]
# print(df2.head())
fn = get_dss() + 'opt/straddle_differ.csv'
if os.path.exists(fn):
df2.to_csv(fn, index=False, mode='a', header=False)
else:
df2.to_csv(fn, index=False)
def examine():
dss = get_dss()
# setting_pz中的品种已在trade_time中维护
#读取交易时段文件
fn = dss + 'fut/cfg/trade_time.csv'
df = pd.read_csv(fn)
tm_pz_set = set(df.symbol)
#print(tm_pz_set)
fn = dss + 'fut/cfg/setting_pz.csv'
df = pd.read_csv(fn)
setting_pz_list = list(df.pz)
#print(setting_pz_list)
for pz in setting_pz_list:
if pz not in tm_pz_set:
to_log('examine: ' + pz +
' of setting_pz.csv not in trade_time.csv')
# 加载配置，目前盯市的品种已在setting_pz中维护
symbols_quote_list = get_symbols_quote()
for symbol in symbols_quote_list:
c = get_contract(symbol)
#print( c.pz )
if c is None:
to_log('examine: ' + symbol +
' of config.json not in setting_pz.csv')
# symbols_quote涵盖symbols_trade
symbols_trade_list = get_symbols_trade()
for symbol in symbols_trade_list:
if symbol not in symbols_quote_list:
to_log('examine: ' + symbol +
' of symbols_trade not in symbols_quote')
# 行情接收器是否存在超时情况
config = open(dss + 'fut/cfg/config.json')
setting = json.load(config)
symbol = setting['symbols_quote_canary']
# symbols_quote_list = symbols.split(',')
now = datetime.now()
today = now.strftime('%Y-%m-%d')
# today = '20200515'
fn = get_dss() + 'fut/tick/tick_' + today + '_' + symbol + '.csv'
if os.path.exists(fn):
df = pd.read_csv(fn)
df = df[(df.UpdateTime >= '14:50:59') & (df.UpdateTime <= '14:59:59')]
df = df.sample(3)
for i, row in df.iterrows():
u = datetime.strptime(row.UpdateDate + ' ' + row.UpdateTime,
'%Y-%m-%d %H:%M:%S')
u += timedelta(seconds=3)
u = datetime.strftime(u, '%Y-%m-%d %H:%M:%S')
# print(row.Localtime, u)
if row.Localtime > u:
to_log('examine: over time! ' + row.Localtime + ' > ' + u)
def iv_straddle_show(symbol, strike_list, startdate, kind):
plt.figure(figsize=(12, 8))
for strike in strike_list:
exchangeID = str(get_contract(symbol).exchangeID)
if exchangeID in ['CFFEX', 'DCE']:
s_a = symbol + '-C-' + strike
s_b = symbol + '-P-' + strike
else:
s_a = symbol + 'C' + strike
s_b = symbol + 'P' + strike
if kind == 'daily':
fn = get_dss() + 'fut/bar/min5_' + s_a + '.csv'
else:
fn = get_dss() + 'fut/put/rec/min5_' + s_a + '.csv'
df_a = pd.read_csv(fn)
if kind == 'daily':
fn = get_dss() + 'fut/bar/min5_' + s_b + '.csv'
else:
fn = get_dss() + 'fut/put/rec/min5_' + s_b + '.csv'
df_b = pd.read_csv(fn)
df_a = df_a[df_a.date >= startdate]
df_b = df_b[df_b.date >= startdate]
assert len(df_a) == len(df_b)
df_a['dt'] = df_a['date'] + ' ' + df_a['time']
df_a['value'] = df_a['close'] + df_b['close']
df_a = df_a.set_index('dt')
# df_a['next'] = df_a['value'].shift(1)
# df_a['value'] = np.log(df_a['value']) - np.log(df_a['next'])
# df_a['value'] = df_a['value'].cumsum()
# print(df_a.head())
plt.plot(df_a.value, label=strike)
plt.xticks(rotation=90)
plt.grid(True, axis='y')
ax = plt.gca()
for label in ax.get_xticklabels():
label.set_visible(False)
for label in ax.get_xticklabels()[1::25]:
label.set_visible(True)
for label in ax.get_xticklabels()[-1:]:
label.set_visible(True)
plt.legend()
fn = 'static/iv_straddle_show.jpg'
plt.savefig(fn)
plt.cla()
r = ''
fn = 'iv_straddle_show.jpg'
now = str(int(time.time()))
r = '<img src=\"static/' + fn + '?rand=' + now + '\" />'
return r
def book_min5_now_show(startdate, dual_list):
now = datetime.now()
startdate = now.strftime('%Y-%m-%d')
plt.figure(figsize=(12, 8))
for dual in dual_list:
symbol_a = dual[0]
num_a = int(dual[1])
symbol_b = dual[2]
num_b = int(dual[3])
fn = get_dss() + 'fut/put/rec/min5_' + symbol_a + '.csv'
if os.path.exists(fn) == False:
continue
df_a = pd.read_csv(fn)
fn = get_dss() + 'fut/put/rec/min5_' + symbol_b + '.csv'
if os.path.exists(fn) == False:
continue
df_b = pd.read_csv(fn)
df_a = df_a[df_a.date >= startdate]
df_b = df_b[df_b.date >= startdate]
df_a = df_a.reset_index()
df_b = df_b.reset_index()
assert len(df_a) == len(df_b)
# print(df_a.head())
# print(df_b.head())
df_a['dt'] = df_a['date'] + ' ' + df_a['time']
df_a['value'] = abs(num_a * df_a['close'] + num_b * df_b['close'])
df_a = df_a.set_index('dt')
# print(df_a.tail())
# print(df_b.tail())
plt.plot(df_a.value,
label=symbol_a + ' ' + str(num_a) + ' ' + symbol_b + ' ' +
str(num_b))
plt.xticks(rotation=90)
plt.grid(True, axis='y')
ax = plt.gca()
for label in ax.get_xticklabels():
label.set_visible(False)
for label in ax.get_xticklabels()[1::25]:
label.set_visible(True)
for label in ax.get_xticklabels()[-1:]:
label.set_visible(True)
plt.legend()
fn = 'static/book_min5_now_show.jpg'
plt.savefig(fn)
plt.cla()
r = ''
fn = 'book_min5_now_show.jpg'
now = str(int(time.time()))
r = '<img src=\"static/' + fn + '?rand=' + now + '\" />'
return r
def pandian_p(today):
# 读取value_p文件
fn_value = get_dss() + 'fut/engine/value_p.csv'
if os.path.exists(fn_value):
df_value = pd.read_csv(fn_value)
#print(df_value)
else:
return
r = []
# p_list = [ 'dali', 'ic', 'opt', 'star', 'cui' ]
p_list = ['dali', 'opt', 'star']
for p in p_list:
df = df_value[df_value.p == p]
# 获得该组合最近日期的一条记录
if len(df) > 0:
rec = df.iloc[-1, :]
capital = rec.capital
date = rec.date
if today[:4] == date[:4]:
newyear_value = rec.newyear_value
else:
newyear_value = rec.cur_value # 换年
else:
continue
# 更新该品种的最新数据
fn = get_dss() + 'fut/engine/' + p + '/portfolio_' + p + '_var.csv'
#fn = get_dss() + 'fut/engine/rsiboll/portfolio_rsiboll_var.csv'
# print(fn)
if os.path.exists(fn):
df = pd.read_csv(fn)
rec = df.iloc[-1, :]
cur_value = rec.portfolioValue
year_ratio = round(100 * (cur_value / newyear_value - 1), 2)
pos_dict = eval(rec.posDict)
close_dict = eval(rec.closeDict)
margin = 0
for symbol in pos_dict:
# print(symbol, pos_dict[symbol], close_dict[symbol])
c = get_contract(symbol)
margin += abs(pos_dict[symbol]) * close_dict[symbol] * float(
c.size) * float(c.margin)
risk = round(100 * (margin / capital), 2)
r.append([
today, p, capital, cur_value, newyear_value, year_ratio,
margin, risk,
str(pos_dict)
])
# print(r)
df = pd.DataFrame(r)
df.to_csv(fn_value, index=False, mode='a', header=None)
def draw_charts():
pz = 'CF'
vtSymbol = 'CF'
fn = get_dss() + 'backtest/fut/' + pz + '/day_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
# df1 = df1[df1.date >= '2019-01-20']
#df1['datetime'] = df1['date'] + ' ' + df1['time']
df1['datetime'] = df1['date']
price_min = int(df1.close.min() * 0.99)
price_max = df1.close.max()
#fn = get_dss( )+ 'fut/engine/aberration_raw/signal_aberration_raw_duo_deal_CF.csv'
#fn = get_dss( )+ 'fut/engine/aberration_raw/signal_aberration_raw_kong_deal_CF.csv'
fn = get_dss(
) + 'fut/engine/aberration_enhance/signal_aberration_enhance_duo_deal_CF.csv'
#fn = get_dss( )+ 'fut/engine/aberration_enhance/signal_aberration_enhance_kong_deal_CF.csv'
df2 = pd.read_csv(fn)
dt_list = df2['datetime'].tolist()
dt_list = [dt[:10] for dt in dt_list]
# print(dt_list)
df2['datetime'] = dt_list
line = gen_line(df1, vtSymbol, price_min, price_max)
line_atr = gen_atr(df1, 10)
line_boll = gen_boll(df1, 10, 2)
line = line.overlap(line_boll)
scatter_open = gen_poit_open(df2)
scatter_close = gen_poit_close(df2)
line = line.overlap(scatter_open)
line = line.overlap(scatter_close)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1300px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="60%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
fn = get_dss() + 'backtest/render/k_deal_aberration_' + vtSymbol + '.html'
grid_chart.render(fn)
def IO(date):
year = date[:4]
hv, obj_price = calc_hv(date)
# print(hv, obj_price)
fn = get_dss() + 'backtest/IO/' + 'IO' + year + '_greeks.csv'
df = pd.read_csv(fn)
df = df[(df.date == date) & (df.symbol.str.startswith('IO'))]
# print(df.head())
# print( len(df) )
term_list = sorted(list(set([x[:6] for x in df.symbol])))
# print(term_list)
r = []
for term in term_list:
df1 = df[df.symbol.str.startswith(term)]
strike_list = sorted(list(set([x[-4:] for x in df1.symbol])))
# print(term, strike_list)
df1 = df1.set_index('symbol')
i = 0
c_iv = 0
p_iv = 0
c_curve_dict = {}
p_curve_dict = {}
for strike in strike_list:
symbol_c = term + '-C-' + strike
symbol_p = term + '-P-' + strike
c_curve_dict[strike] = df1.at[symbol_c, 'iv']
p_curve_dict[strike] = df1.at[symbol_p, 'iv']
if float(strike) >= obj_price - 100 and float(
strike) <= obj_price + 100:
i += 1
c_iv += df1.at[symbol_c, 'iv']
p_iv += df1.at[symbol_p, 'iv']
c_iv = c_iv / i
p_iv = p_iv / i
iv = c_iv * 0.5 + p_iv * 0.5
r.append([
date, term, iv, c_iv, p_iv, hv, obj_price,
str(c_curve_dict),
str(p_curve_dict)
])
fn = get_dss() + 'backtest/IO/' + 'IO' + year + '_sigma.csv'
df = pd.DataFrame(r,
columns=[
'date', 'term', 'iv', 'c_iv', 'p_iv', 'hv',
'obj_price', 'c_curve', 'p_curve'
])
if os.path.exists(fn):
df.to_csv(fn, index=False, mode='a', header=False)
else:
df.to_csv(fn, index=False)
def m15():
year = '2019'
title = 'm15'
symbol1 = 'm01'
symbol2 = 'm05'
start_dt = year + '-09-01'
end_dt = year + '-12-31'
fn1 = get_dss() + 'backtest/fut/m/day_' + symbol1 + '.csv'
fn2 = get_dss() + 'backtest/fut/m/day_' + symbol2 + '.csv'
sp(year + ' - ' + title, symbol1, symbol2, start_dt, end_dt, fn1, fn2)
def p91():
year = '2018'
title = 'p91'
symbol1 = 'p09'
symbol2 = 'p01'
start_dt = year + '-05-01'
end_dt = year + '-08-31'
fn1 = get_dss() + 'backtest/fut/p/day_' + symbol1 + '.csv'
fn2 = get_dss() + 'backtest/fut/p/day_' + symbol2 + '.csv'
sp(year + ' - ' + title, symbol1, symbol2, start_dt, end_dt, fn1, fn2)
def al23():
year = '2019'
title = 'al23'
symbol1 = 'al02'
symbol2 = 'al03'
start_dt = year + '-04-01'
end_dt = year + '-12-31'
fn1 = get_dss() + 'backtest/fut/al/day_' + symbol1 + '.csv'
fn2 = get_dss() + 'backtest/fut/al/day_' + symbol2 + '.csv'
sp(year + ' - ' + title, symbol1, symbol2, start_dt, end_dt, fn1, fn2)
def al67():
year = '2019'
title = 'al67'
symbol1 = 'al11'
symbol2 = 'al12'
start_dt = '2018' + '-01-01'
end_dt = '2019' + '-12-31'
fn1 = get_dss() + 'backtest/fut/al/day_' + symbol1 + '.csv'
fn2 = get_dss() + 'backtest/fut/al/day_' + symbol2 + '.csv'
sp(year + ' - ' + title, symbol1, symbol2, start_dt, end_dt, fn1, fn2)
def star():
pz_list = ['CF', 'SR', 'IO']
for pz in pz_list:
# 读取品种每日盈亏情况，清洗数据为每日一个记录
fn = get_dss() + 'fut/engine/star/portfolio_star_' + pz + '_var.csv'
df2 = pd.read_csv(fn)
df2['date'] = df2.datetime.str.slice(0, 10)
df2['time'] = df2.datetime.str.slice(11, 19)
df2 = df2[df2.time.isin(['14:59:00', '15:00:00'])]
df2 = df2.drop_duplicates(subset=['date'], keep='last')
df2['star'] = df2['portfolioValue'] + df2['netPnl']
df2 = df2.loc[:, ['date', 'star']]
df2 = df2.set_index('date')
# print(df2.head(3))
fn = get_dss(
) + 'fut/engine/mutual/portfolio_mutual_' + pz + '_var.csv'
df3 = pd.read_csv(fn)
df3['date'] = df3.datetime.str.slice(0, 10)
df3['time'] = df3.datetime.str.slice(11, 19)
df3 = df3[df3.time.isin(['14:59:00', '15:00:00'])]
df3 = df3.drop_duplicates(subset=['date'], keep='last')
df3['mutual'] = df3['portfolioValue'] + df3['netPnl']
df3 = df3.loc[:, ['date', 'mutual']]
df3 = df3.set_index('date')
# print(df3.head(3))
df = df2.join(df3)
df['total'] = df['star'] + df['mutual']
# print(df)
plt.figure(figsize=(12, 7))
plt.title(pz)
plt.plot(df.star)
plt.plot(df.mutual)
plt.plot(df.total)
plt.xticks(rotation=45)
plt.grid(True, axis='y')
ax = plt.gca()
for label in ax.get_xticklabels():
label.set_visible(False)
for label in ax.get_xticklabels()[1::25]:
label.set_visible(True)
for label in ax.get_xticklabels()[-1:]:
label.set_visible(True)
plt.legend()
fn = 'static/star_' + pz + '.jpg'
plt.savefig(fn)
plt.cla()
def hs300_spread_show(start_day):
df_300 = get_inx('000300', start_day)
df_300 = df_300.set_index('date')
df_300 = df_300.sort_index()
# print(df_300.tail())
fn = get_dss() + 'fut/cfg/opt_mature.csv'
df_opt = pd.read_csv(fn)
df = df_opt[(df_opt.pz == 'IO') &
(df_opt.flag == df_opt.flag)] # 筛选出不为空的记录
df = df.sort_values('symbol')
symbol_list = list(df.symbol)
# print(symbol_list)
plt.figure(figsize=(12, 8))
plt.title('hs300_spread')
for symbol in symbol_list:
code = 'IF' + symbol[2:]
fn = get_dss() + 'fut/bar/day_' + code + '.csv'
if os.path.exists(fn) == False:
continue
df = pd.read_csv(fn)
df = df[df.date >= start_day]
df = df.set_index('date')
df = df.sort_index()
df['value'] = df.close - df_300.close
# print(df.tail())
plt.plot(df.value, label=code)
plt.xticks(rotation=45)
plt.grid(True, axis='y')
ax = plt.gca()
# for label in ax.get_xticklabels():
# label.set_visible(False)
# for label in ax.get_xticklabels()[1::25]:
# label.set_visible(True)
# for label in ax.get_xticklabels()[-1:]:
# label.set_visible(True)
plt.legend()
fn = 'static/hs300_spread_show.jpg'
plt.savefig(fn)
plt.cla()
r = ''
fn = 'hs300_spread_show.jpg'
now = str(int(time.time()))
r = '<img src=\"static/' + fn + '?rand=' + now + '\" />'
return r
